Multimodal Surveillance Model for Enterovirus D68 Respiratory Disease and Acute Flaccid Myelitis among Children in Colorado, USA, 2022

Surveillance for emerging pathogens is critical for developing early warning systems to guide preparedness efforts for future outbreaks of associated disease. To better define the epidemiology and burden of associated respiratory disease and acute flaccid myelitis (AFM), as well as to provide actionable data for public health interventions, we developed a multimodal surveillance program in Colorado, USA, for enterovirus D68 (EV-D68). Timely local, state, and national public health outreach was possible because prospective syndromic surveillance for AFM and asthma-like respiratory illness, prospective clinical laboratory surveillance for EV-D68 among children hospitalized with respiratory illness, and retrospective wastewater surveillance led to early detection of the 2022 outbreak of EV-D68 among Colorado children. The lessons learned from developing the individual layers of this multimodal surveillance program and how they complemented and informed the other layers of surveillance for EV-D68 and AFM could be applied to other emerging pathogens and their associated diseases.

We compare the observed weekly asthma cases to the model-based forecast by applying a Gaussian CUSUM statistic to the predicted outcome of the forecast model during the target surveillance week.The CUSUM statistic is a widely used surveillance technique for monitoring temporal processes (3).It is calculated by taking the cumulative sum of the standardized residuals of the forecast model and accounting for a downward drift parameter.The drift parameter can be viewed as the minimum change in the temporal process we are interested in detecting, and the value of the drift parameter is typically set to one-half the expected change or can be adjusted empirically (3,8).We calibrated the downward drift parameter by examining the standardized residuals of the forecast model during a previous asthma EV-D68 event in September 2014.During this event, the observed standardized residual was 6.68.Thus, we selected a value of 3.34 for the default drift parameter.However, since this event represented an unprecedented jump in asthma cases, we also monitor changes with the drift parameter set to 2.5 and 1.5, to serve as a medium and high sensitivity variant to the default settings.Values of the standardized residuals of the forecast model in excess of the drift parameter are cumulated by the CUSUM statistic.We designate our asthma surveillance model as statistically out of control if the CUSUM statistic is ≥3 standard residuals; however, in practice we monitor all CUSUM output in excess of 1 standard residual.

Clinical Laboratory Surveillance
EV-D68 surveillance testing on selected rhinovirus/enterovirus-positive specimens from 2015 through August of 2022 was performed as previously described (9,10).In August of 2022, surveillance testing was converted to an updated protocol using a primer-probe set designed to detect subclade B3 as well as previously circulating strains (11).Briefly, specimens selected for surveillance testing were spiked with Exogenous Internal Positive Control DNA (ThermoFisher, Waltham, MA, USA) and subjected to total nucleic acid extraction on the Qiagen EZ1 Advanced XL platform using the Virus 2.0 Mini Kit (Qiagen, Hilden, Germany).cDNA synthesis was performed using TaqMan Reverse Transcription Reagents with a MgCl2 final reaction concentration of 4 mM (ThermoFisher, Waltham, MA, USA) on ABI Veriti Thermal Cycler (ThermoFisher, Waltham, MA, USA).PCR reaction components were as follows: qScript XLT One-step RT-qPCR ToughMix®, Low Rox (Quantabio, Beverly, MA, USA), primers and probes at a final reaction concentration of 250 nM, and internal positive control primers and probe (ThermoFisher, Waltham, MA, USA).Real-time PCR cycling conditions were 45°C x 10 minutes, 95°C x 10 minutes, followed by 45 cycles of: 95°C x 15 seconds and 60°C x 1 minute.
A CT value less-than 40 was considered positive for the EV-D68 target.A sample was considered negative if the EV-D68 target was not detected with a CT value less than 40 and the internal positive control CT value was less than 35.A sample was considered invalid if there was no amplification of the EV-D68 (Appendix Table 1).

Wastewater Surveillance
Extracted wastewater RNA for this study was provided by our collaborators at Colorado State University.The same EV-D68 quantitative PCR primer and probe design described above for clinical samples used to produce an amplicon of 94bps.Primer and probe concentrations and thermocycling conditions were modified for digital PCR on the Qiagen Qiacuity Eight ® Instrument (Qiagen, Hilden, Germany) and analyzed using the instrument software.Briefly, primers and probes were used at a final reaction concentration of 500nM and 250 nM, respectively, and 10µl of extracted RNA was added per 40µl reaction using the Qiagen One-Step RT-PCR ® Kit per manufacturers' guidance.De-identified clinical respiratory specimens previously confirmed to be positive for EV-D68 provided by Children's Hospital Colorado were used as a PCR positive control for this assay.A volume of 10µl of ddH2O spiked into the PCR mastermix was used as a negative control for the PCR assay and only results where the NTC showed no amplification were considered.Digital PCR cycling conditions were 95°C x 10 minutes, 55°C x 5 minutes, followed by 45 cycles of: 95°C x 45 seconds and 55°C x 15 seconds, and a final 35°C for 1 min step.
Qiacuity instrument output is reported in copies/µl of the reaction volume.This output was converted to original sample concentrations by accounting for dilution of extract RNA in the PCR reaction, the concentration factors for Innovaprep concentration of 40mL of sample to ~400ul of eluate (100x) and RNA was then extracted using the Qiagen QiaAmp Kit paired with silica columns (Epoch Biosciences) eluted in ddH2O (2.3x) and scaled to copies/L of original wastewater influent for reporting.The extracted total nucleic acid integrity and concentration of each processed sample was not assessed.
All wastewater samples were collected by a 24-hour composite autosampler and were retrospectively assessed for EV-D68 in singleton using minimal leftover extracted RNA or concentrate frozen from SARS-CoV-2 detection testing processing.Samples were spiked with known quantities of bovine coronavirus (BCoV) as a recovery quality control metric.High level inhibition was absent from any samples tested, with 32.51+/−1.61%BCoV recovery across all three utilities during the study period.Limit of detection on the Qiacuity instrument for this assay was three positive partitions per well and limit of quantification was set at 4,000 copies/L of the EV-D68 target region based on absolute quantified copies/uL obtained using a 10-fold dilution series of de-identified Children's Hospital of Colorado confirmed EV-D68 clinical specimen spiked into ddH2O run in triplicate with acceptable performance criteria defined as the lowest average absolute quantification at which the calculated relative standard deviation was <20%.In silico assessment of specificity was previously published (11) for this assay and was further assessed for this study using the NCBI primer BLAST and BLASTn online tool with no reasonable off-target detection concerns being identified.
Biobanked wastewater samples underwent Innovaprep concentration and were stored as eluate at −70°C for 3-9 months, depending on the sample collection date.All samples were thawed and treated uniformly and compared relative to each other by utility and sample collection date.
Given the lack of a standard viral concentration threshold to determine significant EV-D68 wastewater prevalence, a Bayesian Structural Time Series and Linear Model approach was employed to assess trends in EV-D68 concentration in wastewater over time to inform public health response.This approach was adapted from the current CDC National Wastewater Surveillance System guidance for assessing wastewater trends and parallels the approach used to determine trends in the viral concentration of SARS CoV-2 in Colorado.A trend category is determined based on the model fit for each sample.A 21-day window is used to determine the trend category for each sample; there must be at least five samples within this 21-day period to reliably determine the trend category for each individual sample.When the slope of the trend is greater than 0 and the p-value is <0.05, the trend is classified as increasing; when the slope is less than 0 and the p-value is <0.05, the trend is classified as decreasing; when the p-value is >0.05, the trend is classified as a plateau.